Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations3333
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory286.6 KiB
Average record size in memory88.0 B

Variable types

Numeric8
Categorical3

Alerts

DataPlan is highly overall correlated with DataUsage and 1 other fieldsHigh correlation
DataUsage is highly overall correlated with DataPlan and 1 other fieldsHigh correlation
DayMins is highly overall correlated with MonthlyChargeHigh correlation
MonthlyCharge is highly overall correlated with DataPlan and 2 other fieldsHigh correlation
ContractRenewal is highly imbalanced (54.1%)Imbalance
DataUsage has 1813 (54.4%) zerosZeros
CustServCalls has 697 (20.9%) zerosZeros

Reproduction

Analysis started2024-12-11 02:46:02.057384
Analysis finished2024-12-11 02:46:06.938857
Duration4.88 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

AccountWeeks
Real number (ℝ)

Distinct212
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.06481
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-12-10T21:46:07.000479image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q174
median101
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)53

Descriptive statistics

Standard deviation39.822106
Coefficient of variation (CV)0.39402545
Kurtosis-0.10783598
Mean101.06481
Median Absolute Deviation (MAD)27
Skewness0.096606294
Sum336849
Variance1585.8001
MonotonicityNot monotonic
2024-12-10T21:46:07.093660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 43
 
1.3%
87 42
 
1.3%
101 40
 
1.2%
93 40
 
1.2%
90 39
 
1.2%
95 38
 
1.1%
86 38
 
1.1%
100 37
 
1.1%
116 37
 
1.1%
112 36
 
1.1%
Other values (202) 2943
88.3%
ValueCountFrequency (%)
1 8
0.2%
2 1
 
< 0.1%
3 5
0.2%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
 
0.1%
7 2
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
243 1
 
< 0.1%
232 1
 
< 0.1%
225 2
0.1%
224 2
0.1%
221 1
 
< 0.1%
217 2
0.1%
215 1
 
< 0.1%
212 2
0.1%
210 2
0.1%
209 3
0.1%

ContractRenewal
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
1
3010 
0
323 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3333
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 3010
90.3%
0 323
 
9.7%

Length

2024-12-10T21:46:07.182289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T21:46:07.354931image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 3010
90.3%
0 323
 
9.7%

Most occurring characters

ValueCountFrequency (%)
1 3010
90.3%
0 323
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3333
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3010
90.3%
0 323
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3333
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3010
90.3%
0 323
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3010
90.3%
0 323
 
9.7%

DataPlan
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
0
2411 
1
922 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3333
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2411
72.3%
1 922
 
27.7%

Length

2024-12-10T21:46:07.414302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T21:46:07.477323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2411
72.3%
1 922
 
27.7%

Most occurring characters

ValueCountFrequency (%)
0 2411
72.3%
1 922
 
27.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3333
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2411
72.3%
1 922
 
27.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3333
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2411
72.3%
1 922
 
27.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2411
72.3%
1 922
 
27.7%

DataUsage
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct174
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81647465
Minimum0
Maximum5.4
Zeros1813
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-12-10T21:46:07.548918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.78
95-th percentile3.46
Maximum5.4
Range5.4
Interquartile range (IQR)1.78

Descriptive statistics

Standard deviation1.272668
Coefficient of variation (CV)1.5587355
Kurtosis0.04263016
Mean0.81647465
Median Absolute Deviation (MAD)0
Skewness1.2720573
Sum2721.31
Variance1.6196839
MonotonicityNot monotonic
2024-12-10T21:46:07.636731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1813
54.4%
0.31 41
 
1.2%
0.21 39
 
1.2%
0.29 36
 
1.1%
0.26 34
 
1.0%
0.33 32
 
1.0%
0.32 30
 
0.9%
0.3 29
 
0.9%
0.28 29
 
0.9%
0.27 27
 
0.8%
Other values (164) 1223
36.7%
ValueCountFrequency (%)
0 1813
54.4%
0.11 2
 
0.1%
0.12 3
 
0.1%
0.13 5
 
0.2%
0.14 7
 
0.2%
0.15 5
 
0.2%
0.16 9
 
0.3%
0.17 10
 
0.3%
0.18 7
 
0.2%
0.19 12
 
0.4%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
4.75 1
 
< 0.1%
4.73 1
 
< 0.1%
4.64 1
 
< 0.1%
4.59 1
 
< 0.1%
4.56 3
0.1%
4.48 1
 
< 0.1%
4.46 2
0.1%
4.43 2
0.1%
4.4 1
 
< 0.1%

CustServCalls
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5628563
Minimum0
Maximum9
Zeros697
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-12-10T21:46:07.712050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.315491
Coefficient of variation (CV)0.84172234
Kurtosis1.7309137
Mean1.5628563
Median Absolute Deviation (MAD)1
Skewness1.0913595
Sum5209
Variance1.7305167
MonotonicityNot monotonic
2024-12-10T21:46:07.770893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1181
35.4%
2 759
22.8%
0 697
20.9%
3 429
 
12.9%
4 166
 
5.0%
5 66
 
2.0%
6 22
 
0.7%
7 9
 
0.3%
9 2
 
0.1%
8 2
 
0.1%
ValueCountFrequency (%)
0 697
20.9%
1 1181
35.4%
2 759
22.8%
3 429
 
12.9%
4 166
 
5.0%
5 66
 
2.0%
6 22
 
0.7%
7 9
 
0.3%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
9 2
 
0.1%
8 2
 
0.1%
7 9
 
0.3%
6 22
 
0.7%
5 66
 
2.0%
4 166
 
5.0%
3 429
 
12.9%
2 759
22.8%
1 1181
35.4%
0 697
20.9%

DayMins
Real number (ℝ)

HIGH CORRELATION 

Distinct1667
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.7751
Minimum0
Maximum350.8
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-12-10T21:46:07.839631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.92
Q1143.7
median179.4
Q3216.4
95-th percentile270.74
Maximum350.8
Range350.8
Interquartile range (IQR)72.7

Descriptive statistics

Standard deviation54.467389
Coefficient of variation (CV)0.30297516
Kurtosis-0.019940379
Mean179.7751
Median Absolute Deviation (MAD)36.3
Skewness-0.029077067
Sum599190.4
Variance2966.6965
MonotonicityNot monotonic
2024-12-10T21:46:07.920455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154 8
 
0.2%
159.5 8
 
0.2%
174.5 8
 
0.2%
183.4 7
 
0.2%
175.4 7
 
0.2%
162.3 7
 
0.2%
178.7 6
 
0.2%
194.8 6
 
0.2%
189.3 6
 
0.2%
146.3 6
 
0.2%
Other values (1657) 3264
97.9%
ValueCountFrequency (%)
0 2
0.1%
2.6 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
12.5 1
< 0.1%
17.6 1
< 0.1%
18.9 1
< 0.1%
19.5 1
< 0.1%
25.9 1
< 0.1%
27 1
< 0.1%
ValueCountFrequency (%)
350.8 1
< 0.1%
346.8 1
< 0.1%
345.3 1
< 0.1%
337.4 1
< 0.1%
335.5 1
< 0.1%
334.3 1
< 0.1%
332.9 1
< 0.1%
329.8 1
< 0.1%
328.1 1
< 0.1%
326.5 1
< 0.1%

DayCalls
Real number (ℝ)

Distinct119
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.43564
Minimum0
Maximum165
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-12-10T21:46:08.012625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median101
Q3114
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.069084
Coefficient of variation (CV)0.19982034
Kurtosis0.24318152
Mean100.43564
Median Absolute Deviation (MAD)13
Skewness-0.11178664
Sum334752
Variance402.76814
MonotonicityNot monotonic
2024-12-10T21:46:08.095625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 78
 
2.3%
105 75
 
2.3%
95 69
 
2.1%
107 69
 
2.1%
104 68
 
2.0%
108 67
 
2.0%
97 67
 
2.0%
106 66
 
2.0%
112 66
 
2.0%
110 66
 
2.0%
Other values (109) 2642
79.3%
ValueCountFrequency (%)
0 2
0.1%
30 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
40 2
0.1%
42 2
0.1%
44 3
0.1%
45 3
0.1%
47 2
0.1%
48 3
0.1%
ValueCountFrequency (%)
165 1
 
< 0.1%
163 1
 
< 0.1%
160 1
 
< 0.1%
158 3
0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
152 1
 
< 0.1%
151 5
0.2%
150 6
0.2%
149 1
 
< 0.1%

MonthlyCharge
Real number (ℝ)

HIGH CORRELATION 

Distinct627
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.305161
Minimum14
Maximum111.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-12-10T21:46:08.183537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile33.26
Q145
median53.5
Q366.2
95-th percentile87.8
Maximum111.3
Range97.3
Interquartile range (IQR)21.2

Descriptive statistics

Standard deviation16.426032
Coefficient of variation (CV)0.29173226
Kurtosis-0.016808073
Mean56.305161
Median Absolute Deviation (MAD)10.5
Skewness0.5944978
Sum187665.1
Variance269.81452
MonotonicityNot monotonic
2024-12-10T21:46:08.270572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 84
 
2.5%
46 75
 
2.3%
45 74
 
2.2%
49 73
 
2.2%
54 72
 
2.2%
51 71
 
2.1%
48 68
 
2.0%
53 66
 
2.0%
47 65
 
2.0%
44 62
 
1.9%
Other values (617) 2623
78.7%
ValueCountFrequency (%)
14 1
 
< 0.1%
15.7 1
 
< 0.1%
16 2
 
0.1%
17 1
 
< 0.1%
19 1
 
< 0.1%
20 2
 
0.1%
20.1 1
 
< 0.1%
21 2
 
0.1%
22 5
0.2%
22.8 1
 
< 0.1%
ValueCountFrequency (%)
111.3 1
< 0.1%
110 2
0.1%
108.7 1
< 0.1%
108.6 1
< 0.1%
108.3 1
< 0.1%
106.9 1
< 0.1%
105.6 1
< 0.1%
105.2 1
< 0.1%
104.9 1
< 0.1%
104.7 1
< 0.1%

OverageFee
Real number (ℝ)

Distinct1024
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.051488
Minimum0
Maximum18.19
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-12-10T21:46:08.351975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.94
Q18.33
median10.07
Q311.77
95-th percentile14.22
Maximum18.19
Range18.19
Interquartile range (IQR)3.44

Descriptive statistics

Standard deviation2.5357119
Coefficient of variation (CV)0.25227229
Kurtosis0.025699267
Mean10.051488
Median Absolute Deviation (MAD)1.72
Skewness-0.023845341
Sum33501.61
Variance6.4298349
MonotonicityNot monotonic
2024-12-10T21:46:08.441223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.5 13
 
0.4%
10.19 11
 
0.3%
8.36 11
 
0.3%
8.09 11
 
0.3%
8.39 10
 
0.3%
10.59 10
 
0.3%
10.05 10
 
0.3%
10.47 10
 
0.3%
10.26 10
 
0.3%
8.8 10
 
0.3%
Other values (1014) 3227
96.8%
ValueCountFrequency (%)
0 1
< 0.1%
1.56 1
< 0.1%
2.11 1
< 0.1%
2.13 1
< 0.1%
2.2 1
< 0.1%
2.41 1
< 0.1%
2.46 1
< 0.1%
2.65 1
< 0.1%
2.8 1
< 0.1%
2.93 1
< 0.1%
ValueCountFrequency (%)
18.19 1
< 0.1%
18.09 1
< 0.1%
17.71 1
< 0.1%
17.58 1
< 0.1%
17.55 1
< 0.1%
17.53 1
< 0.1%
17.43 1
< 0.1%
17.37 1
< 0.1%
17.07 1
< 0.1%
17 1
< 0.1%

RoamMins
Real number (ℝ)

Distinct162
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.237294
Minimum0
Maximum20
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2024-12-10T21:46:08.535301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312.1
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation2.7918395
Coefficient of variation (CV)0.27271265
Kurtosis0.60918476
Mean10.237294
Median Absolute Deviation (MAD)1.8
Skewness-0.24513594
Sum34120.9
Variance7.7943681
MonotonicityNot monotonic
2024-12-10T21:46:08.628452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 62
 
1.9%
11.3 59
 
1.8%
9.8 56
 
1.7%
10.9 56
 
1.7%
10.1 53
 
1.6%
10.6 53
 
1.6%
10.2 53
 
1.6%
11 52
 
1.6%
11.1 52
 
1.6%
9.7 51
 
1.5%
Other values (152) 2786
83.6%
ValueCountFrequency (%)
0 18
0.5%
1.1 1
 
< 0.1%
1.3 1
 
< 0.1%
2 2
 
0.1%
2.1 2
 
0.1%
2.2 1
 
< 0.1%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
2.7 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18.9 1
 
< 0.1%
18.4 1
 
< 0.1%
18.3 1
 
< 0.1%
18.2 2
0.1%
18 3
0.1%
17.9 1
 
< 0.1%
17.8 2
0.1%
17.6 2
0.1%
17.5 3
0.1%

Churn
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
0
2850 
1
483 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3333
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2850
85.5%
1 483
 
14.5%

Length

2024-12-10T21:46:08.708677image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-10T21:46:08.766739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2850
85.5%
1 483
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 2850
85.5%
1 483
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3333
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2850
85.5%
1 483
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3333
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2850
85.5%
1 483
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2850
85.5%
1 483
 
14.5%

Interactions

2024-12-10T21:46:06.227768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:02.491029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.095521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.592930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.077044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.566173image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.187319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.702751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:06.310318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:02.564804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.150003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.656073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.136786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.725705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.254956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.766026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:06.373292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:02.623805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.204565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.711075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.195839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.802365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.314296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.832293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:06.433108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:02.771479image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.254668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.766077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.255067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.864018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.373925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.892437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:06.501502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:02.826844image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.318182image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.828827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.316884image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.930780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.437121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.959869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:06.565340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:02.889623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.378166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.892786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.374159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.996259image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.501098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:06.026170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:06.620373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:02.953133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.437413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.947542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.433264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.051909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.570550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:06.089764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:06.688347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.023281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:03.506732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.013236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:04.499742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.120186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:05.634510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-10T21:46:06.153216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-10T21:46:08.811569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AccountWeeksChurnContractRenewalCustServCallsDataPlanDataUsageDayCallsDayMinsMonthlyChargeOverageFeeRoamMins
AccountWeeks1.0000.0000.015-0.0060.0000.0170.0330.0180.012-0.0080.015
Churn0.0001.0000.2580.3160.1000.1080.0480.3550.2940.0800.056
ContractRenewal0.0150.2581.0000.0430.0000.0530.0380.0680.0640.0360.025
CustServCalls-0.0060.3160.0431.0000.018-0.018-0.021-0.015-0.025-0.018-0.017
DataPlan0.0000.1000.0000.0181.0000.9960.0000.0330.7880.0180.000
DataUsage0.0170.1080.053-0.0180.9961.000-0.019-0.0040.6460.0110.077
DayCalls0.0330.0480.038-0.0210.000-0.0191.0000.009-0.009-0.0140.015
DayMins0.0180.3550.068-0.0150.033-0.0040.0091.0000.5990.006-0.016
MonthlyCharge0.0120.2940.064-0.0250.7880.646-0.0090.5991.0000.2890.056
OverageFee-0.0080.0800.036-0.0180.0180.011-0.0140.0060.2891.000-0.003
RoamMins0.0150.0560.025-0.0170.0000.0770.015-0.0160.056-0.0031.000

Missing values

2024-12-10T21:46:06.777248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-10T21:46:06.883451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AccountWeeksContractRenewalDataPlanDataUsageCustServCallsDayMinsDayCallsMonthlyChargeOverageFeeRoamMinsChurn
0128112.701265.111089.09.8710.00
1107113.701161.612382.09.7813.70
2137100.000243.411452.06.0612.20
384000.002299.47157.03.106.60
475000.003166.711341.07.4210.10
5118000.000223.49857.011.036.30
6121112.033218.28887.317.437.50
7147000.000157.07936.05.167.10
8117100.191184.59763.917.588.70
9141013.020258.68493.211.1011.20
AccountWeeksContractRenewalDataPlanDataUsageCustServCallsDayMinsDayCallsMonthlyChargeOverageFeeRoamMinsChurn
3323117100.395118.412645.912.4713.61
3324159100.001169.811446.09.8911.60
332578100.232193.49945.35.859.30
332696100.361106.612846.614.2414.90
332779100.002134.79840.09.4911.80
3328192112.672156.27771.710.789.90
332968100.343231.15756.47.679.60
333028100.002180.810956.014.4414.10
3331184000.002213.810550.07.985.00
333274113.700234.4113100.013.3013.70